# Skill Design and Scoping

Deciding what a skill covers and where it stops, which is the main craft in making one useful.

**Leads to:** Claude Skills

**Rests on nothing.** A place to start.

*AI Agents · AI Coding Assistants*


## Why learn this

Mastering skill design and scoping is the craft that makes a skill genuinely useful instead of a vague, unreliable suggestion box. With this concept, you'll be able to create skills that reliably do exactly what you intend, trigger at the right moments, and combine cleanly with other skills to tackle complex workflows. This is the foundation for everything from efficient coding assistants that make precise multi-file changes to agentic systems that plan and execute tasks autonomously. Without it, every skill you build will feel like a coin flip — working sometimes, but failing in unpredictable ways when the context shifts slightly.


## The intuition

Think of a skill as a well-defined job description for a very diligent but literal-minded employee. If you hand them a job description that says 'handle customer inquiries,' they'll drown in ambiguity — what counts as an inquiry? Which responses are acceptable? When should they escalate? The employee will either freeze, ask for clarification, or guess, and each guess risks being wrong. But if you give them a precise scope: 'Handle refund requests for orders placed within the last 30 days. If the request is older, transfer to the supervisor. If the order is damaged, apply the standard replacement policy.' — they can execute reliably every time. A skill works exactly the same way. It's a set of instructions, packaged with the right context and boundaries, that an AI agent can follow without re-interpreting your intent each time. The scope isn't a restriction — it's what makes the skill dependable. When you define exactly what a skill covers, what it's for, and where it stops, the agent knows when to use it, what to do within it, and when to hand off to something else. Vagueness is the enemy. A skill that tries to do everything will do everything poorly, and worse, it'll be invoked in situations where it doesn't apply, producing nonsense with confidence. Scoping is the art of drawing the line: deciding what belongs inside the skill and what stays outside, so that the skill is sharp, predictable, and reusable.


## An analogy

Consider a well-organized toolbox in a workshop. A chisel is a tool with a narrow scope: it's for carving wood, not for driving nails or measuring lengths. You know exactly when to reach for it, and when you do, it works precisely because it's designed for one thing. Now imagine a 'universal tool' — advertised as handling every job in the workshop. In practice, it's a mess: it's too blunt for carving, too heavy for fine work, and you never know if it's the right choice for the current task. The same principle applies to skills. A skill with a narrow scope — 'generate a unit test for this Python function' — is a chisel. You invoke it when you need that specific thing, and it does it well. A skill with a broad scope — 'help with my project' — is the universal tool. It sounds flexible, but it produces vague, unreliable results because it lacks the specific procedures and context needed for any particular job. The analogy breaks down a little here: tools are physical and static, while skills are procedural and can adapt. A skill can be composed — you can chain a 'parse the requirements' skill with a 'generate the test' skill to handle a broader task. But the analogy holds on the key point: a tool's usefulness depends on its defined purpose and its sharp boundaries; a skill's usefulness depends on its scope and its stopping points.


## Definition

Skill design and scoping is the practice of defining what a skill does, what it doesn't do, and under what conditions it should be invoked, so that the skill behaves consistently and reliably in the hands of an AI agent.


## How it connects

This concept sits at the heart of making AI coding assistants genuinely useful. You likely already have notes on Agent Instruction Files — persistent files that give an agent standing context about a codebase. Skills are closely related: instruction files supply standing context, while skills supply repeatable procedures. Both need careful scoping to avoid vagueness and contradictory instructions. This concept also connects directly to Agentic Test Authoring — a skill that generates tests is only useful if its scope is precise (e.g., 'generate tests for this specific function using these conventions') rather than broad ('improve my test suite'). It also ties into Business Workflow Integration, where a skill that fetches customer data from a CRM has a clear scope — 'fetch a customer record by ID' — and well-defined boundaries that prevent it from accidentally modifying records or exposing sensitive data. Finally, this concept is the design constraint underlying Claude Skills: the books in your library emphasize that focus is the main craft, and that a skill trying to do everything degrades. Scoping is exactly that focus.


## The mathematics

This concept is about drawing a clear line between what a skill should do and what it should not do. In mathematics, this is often called classification. You are sorting items into two groups: the group of things the skill handles (the 'in' set) and the group of things it ignores (the 'out' set). 

Think of it like a filter. A filter lets some things pass through and stops others. When you design a skill, you are building that filter. You define the rules that decide if an input belongs in the 'in' group or the 'out' group.

For example, imagine a skill that writes documentation for Python code. 
- The 'in' set includes: any function written by your team in this project.
- The 'out' set includes: functions from outside libraries, or files that are not code (like settings files).

The math here is simple counting. We measure how well the skill works using a number called reliability. Reliability tells you what fraction of the time the skill does the right thing when it is asked to work.

To calculate reliability, we use three numbers:
1. T (Total): The total number of times the skill was asked to do its job.
2. C (Correct): The number of those times where the task was actually within the skill's scope AND the skill did it correctly.
3. R (Reliability): The result, which is a number between 0 and 1.

The formula is:
R equals C divided by T.

In words: Reliability is the number of correct actions divided by the total number of attempts.

A high reliability score means the skill is trusted. It only says 'yes' when it knows it can succeed. A low reliability score means the skill is confused. It tries to help with things it isn't built for, or it makes mistakes on things it should handle.

When you scope a skill, your goal is to make T and C as close together as possible. You want every time the skill is invoked (T) to be a time where it succeeds (C). If the skill is invoked on tasks it cannot handle, T goes up but C does not, and the reliability score drops.


## A worked example

Let us look at two skills that try to extract customer feedback from support tickets. We will calculate the reliability for each.

Skill A: The Broad Scope Skill
This skill tries to help with any ticket it sees. It does not check if the ticket is about feedback first.

Step 1: Count the total attempts (T).
The system sends 10 tickets to this skill. So, T equals 10.

Step 2: Count the correct actions (C).
Out of these 10 tickets, only 4 are actually about customer feedback. The other 6 are about billing or technical errors. The skill attempts all 10, but it can only be 'correct' on the 4 that are in its intended domain. We assume it handles those 4 correctly. So, C equals 4.

Step 3: Calculate reliability (R).
We divide C by T.
R = 4 divided by 10.
R = 0.4.
This means the skill is reliable only 40% of the time. It is often confused because it tries to work on tickets that are not about feedback.

Skill B: The Narrow Scope Skill
This skill has a strict rule. It only activates if the ticket contains specific words like 'pricing' or 'billing'. If those words are not there, the skill does nothing. It never attempts a task outside its scope.

Step 1: Count the total attempts (T).
The system sends the same 10 tickets to this skill. However, the skill only activates for 3 of them (the ones with 'pricing' or 'billing'). For the other 7, it stays silent. Because reliability measures success *when the skill is active*, we only count the times it was actually invoked. So, T equals 3.

Step 2: Count the correct actions (C).
The skill activates on 3 tickets. All 3 are about feedback. The skill handles all 3 correctly. So, C equals 3.

Step 3: Calculate reliability (R).
We divide C by T.
R = 3 divided by 3.
R = 1.0.
This means the skill is reliable 100% of the time it acts.

Comparison:
Skill A has a reliability of 0.4.
Skill B has a reliability of 1.0.

Even though Skill B handles fewer tasks (only 3 tickets instead of 10), it is much more useful because you can trust it. When it speaks, it is right. Skill A tries to do everything but fails often. Good scoping means saying 'no' to most things so that your 'yes' is always correct.


## From scratch, in Python

Here's a plain Python example that shows how a skill's scope can be evaluated using simple lists and a function.

```python
# We define a list of test tasks
all_tasks = [
    {"task": "write a unit test for function calculate_total", "type": "testing"},
    {"task": "fix a bug in the login flow", "type": "debugging"},
    {"task": "generate documentation for module utils", "type": "documentation"},
    {"task": "implement a new API endpoint", "type": "feature"},
]

# Our skill's scope: it only handles tasks of type "testing"
allowed_types = ["testing"]

# A function that checks if a task is within scope
def is_in_scope(task):
    return task["type"] in allowed_types

# Iterate through all tasks and print which ones are in scope
for task in all_tasks:
    if is_in_scope(task):
        print(f"IN SCOPE: {task['task']}")
    else:
        print(f"OUT OF SCOPE: {task['task']}")
```


## What the code does

First, we define a list called `all_tasks`. In Python, a **list** is a collection that keeps things in order and can be changed. Each item in the list is a **dictionary** — a collection that looks things up by name rather than by position. So `{"task": "write a unit test...", "type": "testing"}` is one dictionary with two keys: `task` and `type`. The next line, `allowed_types = ["testing"]`, is another list holding a single string, `"testing"`. Then we define a **function** named `is_in_scope` — a reusable block of code that takes one input (`task`) and returns `True` or `False` based on whether the task's `type` is in the `allowed_types` list. The `return` statement sends that answer back to wherever the function was called. Finally, we use a **for loop** to go through each `task` in `all_tasks` one by one. Inside the loop, `if is_in_scope(task):` checks the function's result, and depending on the answer, we print either "IN SCOPE" or "OUT OF SCOPE" using an f-string (the `f` before the quotes lets us insert the value of `task['task']`). This shows the core idea: a skill's scope can be encoded as a list of allowed types or conditions, and the agent consults that list to decide whether to act.


## Common misconceptions

**A skill needs to handle every possible request to be useful.** — If my skill is too narrow, people won't use it because it can't do everything.

A skill that tries to cover too many scenarios becomes vague and unreliable. It gets invoked for things it wasn't designed for, leading to poor results and user frustration. Narrow scope is a feature, not a bug. In fact: A well-scoped skill does fewer things but does them excellently. You can always compose multiple narrow skills to cover a broader workflow.

**Bias in a skill means it's unfair.** — If my skill has a bias, it's probably discriminating against some inputs.

In this context, 'bias' is a technical term from the mathematics of neurons, not about fairness. But here we're talking about skills, not neurons. The confusion arises from language. In fact: When we talk about skill scoping, we don't use the word 'bias' at all. We talk about scope, boundaries, and trigger conditions. The skill's behavior is determined by its instructions, not by any inherent bias.

**A skill with a broad scope is more flexible and therefore better.** — I want my skill to be able to adapt to any situation, so I'll make it general.

General skills lack the specific procedures and context needed for any particular job. They produce vague, unreliable results because they don't know what to do in each specific case. In fact: Flexibility comes from composing narrow skills. Each narrow skill handles one thing perfectly, and you chain them together to handle complex workflows.

**Trigger conditions are optional.** — The skill's description is enough; the agent will know when to use it.

Without explicit trigger conditions — keywords, patterns, or input validation — the agent has to guess. Guessing leads to invoking the skill at the wrong times, or missing it when it should be used. In fact: Always define clear trigger conditions in the skill's description or metadata. The agent uses these to decide when to activate the skill.

**One big skill is better than several small ones.** — If I have one skill that does documentation, testing, and debugging, it's more efficient to maintain.

A monolithic skill violates the principle of composition. It's harder to test, harder to update, and more likely to have conflicting instructions. When something changes, you have to edit the whole thing, risking breakage in other areas. In fact: Compose small, focused skills. Each one has a single responsibility. When something changes, you only update the relevant skill, and you can reuse skills across different workflows.

**A skill's scope is fixed once it's written.** — Once I've defined the scope, I can't change it without breaking everything.

Skill scoping is an iterative process. As you use the skill, you discover edge cases and refine the scope. Evals help you catch problems and iterate. In fact: Review and update your skills regularly. Use evals to test them on new inputs and adjust the scope to improve reliability.


## In short

Scoping is the main craft in skill design. A skill that tries to do everything degrades into vagueness; a skill with a narrow, precise scope is reliable and invokable. Define what the skill covers, what it doesn't, and under what conditions it should activate. Compose small skills to handle complex workflows. Test your skills with evals to keep them sharp.


## Where this is used

Skill design and scoping is used everywhere in modern AI, especially in coding assistants and agentic systems. In large language models like ChatGPT or Claude, skills are often implemented as system prompts or tools. A developer writes a skill definition with a clear name, description, and input schema — that description is the scoping. For example, a skill named `generate_unit_tests` has a description like "Use this skill when the user asks to write tests for a Python function. Do not use it for debugging or refactoring." The model reads that description and decides when to invoke the skill. If the scope is vague — "help with code" — the model will invoke it constantly, even when it's not appropriate, degrading the quality of the response.

In small language models that run on devices like phones or laptops, scoping becomes even more critical. These models have limited context windows and computational power, so a skill that fires too often wastes resources. A scoped skill with tight trigger conditions — for example, only activate when the user says "extract names from this document" — ensures the model only does heavy processing when needed.

In retrieval-augmented generation (RAG), skills are often used to decide which documents to retrieve. A skill scoped to "retrieve legal contracts when the query mentions 'contract', 'agreement', or 'clause'" will do a better job than a broad "retrieve anything relevant" skill. The scoping defines the search keywords and the data source, so the retrieval is precise and doesn't pull irrelevant information.

In agentic systems that plan and call tools, skill scoping is the backbone. An agent might have a toolkit of skills: `search_web`, `read_file`, `execute_code`, `send_email`. Each skill has a clear scope — `send_email` only works if the user has provided a recipient and subject, and it should never be used for calendar events. Without that scoping, the agent might call `send_email` to schedule a meeting, creating chaos. The developer writes these scopes as part of the skill definitions, and the agent's planning loop checks them before each action.

In everyday applied machine learning, scoping shows up in how you define the problem. A fraud detection model trained on credit card transactions has a clear scope: it detects fraudulent transactions, not loan defaults. The features, the training data, and the decision threshold all define that scope. A developer building such a model writes code to preprocess transaction data, selects features like amount and location, and sets a threshold for flagging. If the scope is too broad — "detect any financial anomaly" — the model will produce false positives constantly. Scoping is what makes the model useful in practice.

What does building software with skills look like? A developer uses a framework like Claude Skills or LangChain to define a skill. They write a YAML or JSON file describing the skill's name, description, input arguments, and the instructions the model should follow. They also write a set of evals — test cases that verify the skill works correctly. For example, for a skill that summarizes meeting notes, the eval might include a sample transcript and a expected summary. If the skill's scope is too broad, the eval will fail because the skill produces inconsistent results on out-of-scope inputs. The developer iterates on the scope — narrowing the description, adding trigger examples, and defining stopping conditions — until the evals pass consistently. When it's done badly, the skill is invoked at the wrong times, produces vague or incorrect outputs, and erodes trust in the entire system.


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Read this on the web: https://basavapurushottam.com/learn-ai/skill-design-and-scoping
